Edit StudentProjectForm: Uncertainty quantification for data driven clinical decision making

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Summary:	MANDATORY: (provide a one-line* summary of the project)*
Programme:	OPTIONAL: (Name and the number of credits)
Keywords:	OPTIONAL: (Give 4-5 keywords)
TimeFrame:	MANDATORY: (Indicate time for the project, for example, Fall 2024)
References:	Sensoy, Murat, Lance Kaplan, and Melih Kandemir. "Evidential deep learning to quantify classification uncertainty." arXiv preprint arXiv:1806.01768 (2018). Amini, Alexander, et al. "Deep evidential regression." arXiv preprint arXiv:1910.02600 (2019). OPTIONAL: (Give 2-3 references for students to start with)
Prerequisites:	Statistics; Neural Networks; Programming (Python or Matlab) OPTIONAL: (Courses, any other important information)
Supervisor(s):	MANDATORY: (can be multiple names, coma-separated)
Examiner:	OPTIONAL: (Name of project Examiner)
Author(s):	OPTIONAL: (can be multiple names, coma-separated)
Level:	MANDATORY: choose one, preferably <Master>
Status:	MANDATORY: choose <Open> when the topic is ready for students to select

Project description (free text)

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Neural networks are increasingly being used in many safety critical decision processes, thereby necessitating reliable uncertainty estimates along with predictions. Healthcare is no different and reliable and accurate predictions carry great importance, since they may contribute to an incorrect clinical decision risking a human life in addition to severe ethical and financial costs. Data for the project comes from MIMIC-III (an open source EHR database) The student will go through the state-of-the-art in Bayesian and Evidential Learning to quantify prediction uncertainties. Focus will be on identifying and understanding when the model says "I don't know". For questions contact Awais Ashfaq (awais.ashfaq@hh.se)

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Edit StudentProjectForm: Uncertainty quantification for data driven clinical decision making

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